Gaiyun He

Simulation and analysis for accuracy predication and adjustment for machine tool assembly process

This article presents an approach to investigate the variation propagation of machine tools due to the geometric errors produced in assembly process and determine a pre-adjustment method in assembly design stage. At the beginning, a state-space model was used to describe the variation propagation in machine tool assembly process. Subsequently, a finite element analysis consistent with a selected assembly sequence was conducted, including the components in their unassembled state which is always ignored in the existing study. A horizontal machine center was taken as an example to clarify the proposed method. The guide rail deformations in normal direction were defined to obtain the joint kinematic errors in each assembly station. Based on this, an analysis calculation is formulated to determine the total deviation in assembly and then the adjustment before assembling was identified to reduce the assembly errors. The method has strong feasibility and practicality, and when this method is adopted, the static deformation error produced in assembly process would be decreased obviously and can effectively improve the precision of machine tools assembly. The proposed method was eventually applied to the assembly of a horizontal machine center, and the final evaluation of accuracy in our experiments can meet the requirements well.

Identification and Adjustment of Guide Rail Geometric Errors Based on BP Neural Network

Abstract The relative positions between the four slide blocks vary with the movement of the table due to the geometric errors of the guide rail. Consequently, the additional load on the slide blocks is increased. A new method of error measurement and identification by using a self-designed stress test plate was presented. BP neural network model was used to establish the mapping between the stress of key measurement points on the test plate and the displacements of slide blocks. By measuring the stress, the relative displacements of slide blocks were obtained, from which the geometric errors of the guide rails were converted. Firstly, the finite element model was built to find the key measurement points of the test plate. Then the BP neural network was trained by using the samples extracted from the finite element model. The stress at the key measurement points were taken as the input and the relative displacements of the slide blocks were taken as the output. Finally, the geometric errors of the two guide rails were obtained according to the measured stress. The results show that the maximum difference between the measured geometric errors and the output of BP neural network was 5 μm. Therefore, the correctness and feasibility of the method were verified.